Highway pavement is typically a layered structure consisting of surface courses, a base, and a sub-base all deposited on the sub-grade. Deterioration of pavement results not only from surface cracks and potholes due to tire friction, but also from de-bonding or stripping of sub-surface layers due to material aging. Sub-surface initialized defects often develop before surface cracks are visible and lead to surface damage. According to the American Society of Civil Engineers (ASCE) Infrastructure Report Card in 2009, United States bridges received a grade “C” and roads received a grade “D−”. Therefore, early detection and repair of the hidden sub-surface defects is of great importance in maintaining roadways.
Methods have been developed and utilized for sub-surface sensing, including Impact Echo (IE), Impulse Response (IR), Ground Penetrating Radar (GPR), Chain-drag, and Spectral Analysis of Surface Waves (SASW). IE is able to identify the de-bonding and properties of a shallow top layer. IR can test the overall dynamic stiffness/mobility of the entire pavement structure. GPR is best for locating metal materials, such as reinforcement rebar. Chain-drag can be used to find de-bonded areas through detecting the resulting hollow sound. SASW and its related methods are very popular for the ability to estimate the depth and elastic modulus of sub-surface layers.
Since first proposed in the 1980's, SASW has been widely applied in geology field tests for estimating the underground soil profile without coring or opening the ground. It utilizes the dispersion features of the surface wave that propagates horizontally in the soil when subject to an impact load. The dispersion curve represents the relationship between the wave speed, and wavelength or frequency. Once the dispersion curve is obtained from the test data, the layer profile and shear velocities can be estimated by inverting algorithms.
Efforts have been made to improve the accuracy and efficiency of SASW. For example, a stiffness matrices method was developed to perform inversion analysis to investigate pavement systems and concrete structures. Other methods based on the SASW principal have been developed, including the Multichannel Analysis of Surface Wave (MASW) method in which multiple sensors to record the complete wave field and resolve the different wave modes.
One major issue that negatively impacts the efficiency of the prior SASW methods is the iterative inversion process, which is typically time consuming and requires human expertise to set the initial and adjusted values of the elastic modulus profile. Consequently, these prior SASW methods are limited to being point-to-point, posted-processed stationary tests. Research is being done towards identifying faster and/or automated inversion analysis algorithms to enhance efficiency. One such algorithm constructs the dispersion curve through fitting a complex-valued curve to the phase information of the cross power spectra using a coherence function as a weighting function. In another, a Monte Carlo algorithm and maximum likelihood method were chosen to examine the possible solutions with minimal constraints and to estimate the uncertainties of the resulting model parameters. In order to identify the predominant propagation modes easily, an inversion method based on the maximum vertical flexibility coefficient was introduced. In addition, an algorithm called the peak-trough and frequency-wave number (PT/FW) technique was developed to determine the phase velocity more effectively as compared to the traditional phase difference method. Moreover, Genetic Algorithm (GA)-based inversion and combination of genetic and linearized algorithms in a two-step joint inversion have also been employed in recent years.
However, all of these improvements only modified the method of initializing and adjusting the assumed profile for quick convergence. The inversion still relies on the basic procedure of guessing first and then checking with forward analysis. A fast inversion algorithm named fast simulated annealing (FSA) global search algorithm minimizes the difference between the measured phase-velocity spectrum and the spectrum calculated from a theoretical layer model, including the field setup geometry. However, it is limited to resolving the properties of the first layer only.